code: def fit(self, X, y, max_epochs=10):
      # The fit method performs the actual optimization
      X_torch = torch.Tensor(X)
      y_torch = torch.Tensor(y)
  
      for e in range(max_epochs):
          self.optimizer.zero_grad()
          # Forward pass
          y_pred = self.forward(X_torch)
          # Compute Loss
          loss = self.loss(y_pred, y_torch).mean()
          # Backward pass
          loss.backward()
          self.optimizer.step()
  
file_path: tslearn/docs/examples/autodiff/plot_soft_dtw_loss_for_pytorch_nn.py
imports: import numpy
  import matplotlib.pyplot
  from tslearn.datasets import CachedDatasets
  from tslearn.metrics import SoftDTWLossPyTorch
  import torch
  from torch import nn
label_desc: Use image node_id 3 for calling the MultiLayerPerceptron obj's underlying member method code with example usage: obj.fit(X, y, max_epochs) without return types